Allison, P. D. (1987). Estimation of linear models with incomplete data. Sociology Methodology 17, 71–103.
Baker, S. G. and Laird, N. M. (1988). Regression analysis for categorical variables with outcome subject to non-ignorable non-response. Journal of the American Statistical Association 83, 62–69.
Baker, S. G., Rosenberger, W. F., and DerSimonian, R. (1992). Closed-form estimates for missing counts in two-way contingency tables. Statistics in Medicine 11, 643–657.
Beunckens, C., Molenberghs, G., Verbeke, G., and Mallinckrodt, C. (2007). A latent-class mixture model for incomplete longitudinal Gaussian data. Biometrics. To appear.
Catchpole, E. A. and Morgan, B. J. T. (1997). Detecting parameter redundancy. Biometrika 84, 187–196.
Catchpole, E. A., Morgan, B. J. T., and Freeman, S. N. (1998). Estimation in parameter-redundant models. Biometrika 85, 462–468.
Cochran, W. G. (1977). Sampling Techniques. New York: Wiley.
Cohen, J. and Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Erlbaum.
Cook, R. D. (1986). Assessment of local influence. Journal of the Royal Statistical Society, Series B 48, 133–169.
Copas, J. B. and Li, H. G. (1997). Inference from non-random samples (with discussion). Journal of the Royal Statistical Society, Series B 59, 55–96.
Diggle, P. J. and Kenward, M. G. (1994). Informative drop-out in longitudinal data analysis (with discussion). Applied Statistics 43, 49–93.
Draper, D. (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society, Series B 57, 45–97.
Ekholm, A. and Skinner, C. (1998). The Muscatine children’s obesity data reanalysed using pattern mixture models. Applied Statistics 47, 251–263.
Fitzmaurice, G. M., Molenberghs, G., and Lipsitz, S. R. (1995). Regression models for longitudinal binary responses with informative dropouts. Journal of the Royal Statistical Society, Series B 57, 691–704.
Follmann, D. and Wu, M. (1995). An approximate generalized linear model with random effects for informative missing data. Biometrics 51, 151–168.
Foster, J. J. and Smith, P. W. F. (1998). Model-based inference for categorical survey data subject to non-ignorable non-response. Journal of the Royal Statistical Society, Series B 60, 57–70.
Gelman, A., Van Mechelen, I., Verbeke, G., Heitjan, D. F., and Meulders, M. (2005). Multiple imputation for model checking: Completed-data plots with missing and latent data. Biometrics 61, 74–85.
Glynn, R. J., Laird, N. M., and Rubin, D. B. (1986). Selection modelling versus mixture modelling with non-ignorable nonresponse. In H. Wainer (ed.), Drawing Inferences from Self Selected Samples, pp. 115–142. New York: Springer-Verlag.
Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5, 475–492.
Hedeker, D. and Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods 2, 64–78.
Hogan, J. W. and Laird, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 16, 239–258.
International Conference on Harmonisation E9 Expert Working Group (1999). Statistical principles for clinical trials: ICH Harmonised Tripartite Guideline. Statistics in Medicine 18, 1905–1942.
Jansen, I., Beunckens, C., Molenberghs, G., Verbeke, G., and Mallinckrodt, C. (2006a). Analyzing incomplete discrete longitudinal clinical trial data. Statistical Science 21, 52–69.
Jansen, I., Hens, N., Molenberghs, G., Aerts, M., Verbeke, G., and Kenward, M. G. (2006b). The nature of sensitivity in missing not at random models. Computational Statistics and Data Analysis 50, 830–858.
Jennrich, R. I. and Schluchter, M. D. (1986). Unbalanced repeated measures models with structured covariance matrices. Biometrics 42, 805–820.
Kenward, M. G. (1998). Selection models for repeated measurements with nonrandom dropout: An illustration of sensitivity. Statistics in Medicine 17, 2723–2732.
Kenward, M. G., Goetghebeur, E. J. T., and Molenberghs, G. (2001). Sensitivity analysis of incomplete categorical data. Statistical Modelling 1, 31–48.
Kenward, M. G. and Molenberghs, G. (1999) Parametric models for incomplete continuous and categorical longitudinal studies data. Statistical Methods in Medical Research 8, 51–83.
Kenward, M.G., Molenberghs, G., and Thijs, H. (2003). Pattern-mixture models with proper time dependence. Biometrika 90, 53–71.
Laird, N. M. (1994). Discussion of “Informative dropout in longitudinal data analysis,” by P. J. Diggle and M. G. Kenward. Applied Statistics 43, 84.
Lesaffre, E. and Verbeke, G. (1998). Local influence in linear mixed models. Biometrics 54, 570–582.
Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125–134.
Little, R. J. A. (1994a). A class of pattern-mixture models for normal incomplete data. Biometrika 81, 471–483.
Little, R. J. A. (1994b). Discussion of “Informative dropout in longitudinal data analysis,” by P. J. Diggle and M. G. Kenward. Applied Statistics 43, 78.
Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112–1121.
Little, R. J. A. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd ed. New York: Wiley.
Little, R. J. A. and Wang, Y. (1996). Pattern-mixture models for multivariate incomplete data with covariates. Biometrics 52, 98–111.
Little, R. J. A. and Yau, L. (1996). Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics 52, 1324–1333.
McArdle, J. J. and Hamagami, F. (1992). Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. Experimental Aging Research 18, 145–166.
Michiels, B., Molenberghs, G., and Lipsitz, S. R. (1999). Selection models and pattern-mixture models for incomplete categorical data with covariates. Biometrics 55, 978–983.
Michiels, B., Molenberghs, G., Bijnens, L., and Vangeneugden, T. (2002). Selection models and pattern-mixture models to analyze longitudinal quality of life data subject to dropout. Statistics in Medicine 21, 1023–1041.
Molenberghs, G. and Kenward, M. G. (2007). Missing Data in Clinical Studies. Chichester: Wiley.
Molenberghs, G., Kenward, M. G., and Goetghebeur, E. (2001). Sensitivity analysis for incomplete contingency tables: The Slovenian plebiscite case. Applied Statistics 50, 15–29.
Molenberghs, G., Kenward, M. G., and Lesaffre, E. (1997). The analysis of longitudinal ordinal data with non-random dropout. Biometrika 84, 33–44.
Molenberghs, G., Michiels, B., and Kenward, M. G. (1998). Pseudo-likelihood for combined selection and pattern-mixture models for missing data problems. Biometrical Journal 40, 557–572.
Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
Molenberghs, G., Michiels, B., Kenward, M. G., and Diggle, P. J. (1998). Missing data mechanisms and pattern-mixture models. Statistica Neerlandica 52, 153–161.
Molenberghs, G., Goetghebeur, E. J. T., Lipsitz, S. R., and Kenward, M. G. (1999). Non-random missingness in categorical data: strengths and limitations. American Statistician 53, 110–118.
Molenberghs, G., Verbeke, G., Thijs, H., Lesaffre, E., and Kenward, M. G. (2001). Mastitis in dairy cattle: Influence analysis to assess sensitivity of the dropout process. Computational Statistics and Data Analysis 37, 93–113.
Molenberghs, G., Thijs, H., Jansen, I., Beunckens, C., Kenward, M. G., Mallinckrodt, C., and Carroll, R. J. (2004). Analyzing incomplete longitudinal clinical trial data. Biostatistics 5, 445–464.
Molenberghs, G., Beunckens, C., Sotto, C., and Kenward, M. G. (2007). Every missing not at random model has got a missing at random counterpart with equal fit. Journal of the Royal Statistical Society, Series B. To appear.
Muthén, B., Kaplan, D., and Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika 52, 431–462.
Nordheim, E. V. (1984). Inference from nonrandomly missing categorical data: An example from a genetic study on Turner’s syndrome. Journal of the American Statistical Association 79, 772–780.
Potthoff, R. F. and Roy, S. N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51, 313–326.
Rizopoulos D., Verbeke G., and Lesaffre E. (2007). Sensitivity analysis in pattern mixture models using the extrapolation method. Submitted for publication.
Rizopoulos D., Verbeke G., and Molenberghs G. (2008). Shared parameter models under randomeffects misspecification. Biometrika 94, 94–95.
Robins, J. M. and Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90, 122–129.
Robins, J. M., Rotnitzky, A., and Scharfstein, D. O. (1998). Semiparametric regression for repeated outcomes with non-ignorable non-response. Journal of the American Statistical Association 93, 1321–1339.
Robins, J. M., Rotnitzky, A., and Scharfstein, D. O. (2000). Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In M. E. Halloran and D. A. Berry (eds.), Statistical Models in Epidemiology, the Environment, and Clinical Trials, pp. 1–94. New York: Springer.
Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 89, 846–866.
Rotnitzky, A., Scharfstein, D., Su, T. L., and Robins, J. M. (2001). Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring. Biometrics 57, 103–113.
Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581–592.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
Rubin, D. B. (1994). Discussion of “Informative dropout in longitudinal data analysis,” by P. J. Diggle and M. G. Kenward. Applied Statistics 43, 80–82.
Rubin, D. B., Stern H. S., and Vehovar V. (1995). Handling “don’t know” survey responses: The case of the Slovenian plebiscite. Journal of the American Statistical Association 90, 822–828.
Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association 94, 1096–1146.
Thijs, H., Molenberghs, G., and Verbeke, G. (2000). The milk protein trial: Influence analysis of the dropout process. Biometrical Journal 42, 617–646.
Thijs, H., Molenberghs, G., Michiels, B., Verbeke, G., and Curran, D. (2002). Strategies to fit pattern-mixture models. Biostatistics 3, 245–265.
Troxel, A. B., Harrington, D. P., and Lipsitz, S. R. (1998). Analysis of longitudinal data with non-ignorable non-monotone missing values. Applied Statistics 47, 425–438.
Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data. New York: Springer.
Tsonaka R., Verbeke G., and Lesaffre E. (2007). A semi-parametric shared parameter model to handle non-monotone non-ignorable missingness. Submitted for publication.
Vach, W. and Blettner, M. (1995). Logistic regression with incompletely observed categorical covariates — Investigating the sensitivity against violation of the missing at random assumption. Statistics in Medicine 12, 1315–1330.
Vansteelandt, S., Goetghebeur, E., Kenward, M. G., and Molenberghs, G. (2006). Ignorance and uncertainty regions as inferential tools in a sensitivity analysis. Statistica Sinica 16, 953–979.
Van Steen, K., Molenberghs, G., Verbeke, G., and Thijs, H. (2001). A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data. Statistical Modelling 1, 125–142.
Verbeke, G., Lesaffre, E., and Spiessens, B. (2001). The practical use of different strategies to handle dropout in longitudinal studies. Drug Information Journal 35, 419–434.
Verbeke, G. and Molenberghs, G. (1997). Linear Mixed Models in Practice: A SAS-Oriented Approach, Lecture Notes in Statistics 126. New York: Springer.
Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E., and Kenward, M. G. (2001). Sensitivity analysis for non-random dropout: A local influence approach. Biometrics 57, 7–14.
White, I. R. and Goetghebeur, E. J. T. (1998). Clinical trials comparing two treatment arm policies: Which aspects of the treatment policies make a difference? Statistics in Medicine 17, 319–340.
Wu, M. C. and Bailey, K. R. (1988). Analysing changes in the presence of informative right censoring caused by death and withdrawal. Statistics in Medicine 7, 337–346.
Wu, M. C. and Bailey, K. R. (1989). Estimation and comparison of changes in the presence of informative right censoring: Conditional linear model. Biometrics 45, 939–955.
Wu, M. C. and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 44, 175–188.